DeepSeek V4.1 Flash vs Gemini Computer Use
At a Glance
| Compare |  DeepSeek V4.1 FlashDeepSeek |  Gemini Computer UseGoogle DeepMind |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| CostLower is better · Published-token output estimate | #5 of 44$0.022 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.15DeepSeek ↗ · Sep 10, 2026 | $1.25Google AI ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | $0.60Deepinfra ↗ · Sep 21, 2026 | $10.00Google AI ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 1,049K | 128K |
| Model facts checked | Sep 10, 2026View model evidence → | Aug 29, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | DeepSeek-V4.1-Flash | Gemini Computer Use |
|---|---|---|
| Developer | DeepSeek | Google DeepMind |
| Family | Deepseek V4 1 | Gemini Tools |
| Model | DeepSeek-V4.1-Flash | Gemini Computer Use |
| Version | DeepSeek-V4.1-Flash | Gemini Computer Use |
| Lifecycle | active | preview |
| Released | 2026-09-10 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,049K | 128K |
| Total parameters | 763.2B | Unknown |
| Active parameters | Unknown | Unknown |
| License | mit | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | DeepSeek (Standard), Deepinfra (Standard), Together Ai (Standard) | Google AI (Standard), Google Gemini (Standard) |
| Capabilities | agents, chat, fim, generation, reasoning, responses, structured_outputs, tools, vision | generation, reasoning, tools |
| Architecture design | Causal Encoder-Decoder (20 encoder + 20 decoder layers) | Unknown |
| Backbone parameters | 552000000000 parameters | Unknown |
| Active parameters during decode | 16000000000 parameters | Unknown |
| Active parameters during prefill | 8000000000 parameters | Unknown |
| Pre-training corpus | 45000000000000 tokens | Unknown |
| Reasoning effort range | 1–100 | Unknown |
| Routed experts per MoE layer | 384 experts | Unknown |
| Routed experts per token | 6 experts | Unknown |
| Transformer layers | 40 layers | Unknown |
DeepSeek V4.1 Flash Capabilities
Gemini Computer Use Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
DeepSeek-V4-FlashDeepSeek | vs | DeepSeek-V4.1-FlashDeepSeek | family variantstext |
DeepSeek-V4.1-FlashDeepSeek | vs |  GPT-6 AstraOpenAI | cross-developer peersimage, text |
Claude Fable 5.1Anthropic | vs | DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs | Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
DeepSeek-V4.1-FlashDeepSeek | vs | Grok 4.6xAI | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs |  Qwen3.8-MaxQwen | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs |  GLM-5.3Z.ai | cross-developer peerstext |
DeepSeek-V4.1-FlashDeepSeek | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
DeepSeek V4.1 Flash vs Gemini Computer Use FAQs
Is DeepSeek V4.1 Flash or Gemini Computer Use better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4.1 Flash and Gemini Computer Use, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V4.1 Flash or Gemini Computer Use?+
DeepSeek V4.1 Flash is $0.15 and Gemini Computer Use is $1.25 per million tokens, so DeepSeek V4.1 Flash is cheaper on this metric. DeepSeek V4.1 Flash is $0.60 and Gemini Computer Use is $10.00 per million tokens, so DeepSeek V4.1 Flash is cheaper on this metric.
Which has a larger context window, DeepSeek V4.1 Flash or Gemini Computer Use?+
DeepSeek V4.1 Flash has the larger sourced context window. DeepSeek V4.1 Flash supports 1,049K and Gemini Computer Use supports 128K.
Which performs better in benchmarks, DeepSeek V4.1 Flash or Gemini Computer Use?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can DeepSeek V4.1 Flash or Gemini Computer Use be self-hosted?+
DeepSeek V4.1 Flash is the only model in this pair currently marked as self-hostable. DeepSeek V4.1 Flash is open weight; Gemini Computer Use is not marked open weight.
Can DeepSeek V4.1 Flash and Gemini Computer Use understand images?+
DeepSeek V4.1 Flash is documented with image input; Gemini Computer Use is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, DeepSeek V4.1 Flash or Gemini Computer Use?+
DeepSeek V4.1 Flash has the larger sourced maximum output: DeepSeek V4.1 Flash supports 393K and Gemini Computer Use supports 64K output tokens.
Do DeepSeek V4.1 Flash and Gemini Computer Use support reasoning and tool use?+
DeepSeek V4.1 Flash: reasoning, tool calling, and image input. Gemini Computer Use: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V4.1 Flash or Gemini Computer Use?+
DeepSeek V4.1 Flash has 3 sourced provider routes; Gemini Computer Use has 2, so DeepSeek V4.1 Flash has broader tracked availability.
Which offers better value, DeepSeek V4.1 Flash or Gemini Computer Use?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.